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<p>The Internet of Things (IoT) involves internet-connected devices capable of sharing information, leading to increased network traffic. Efficient network management requires flexibility, reliability, energy conservation, and effective data traffic handling for seamless service delivery. Load balancing helps solve many network issues by distributing traffic effectively and reducing congestion. As the traditional networks possess challenges in centralized control for device management and protocol updates, Software-Defined Networking manages network devices, offering comprehensive network control, separating data and control elements for programmability. This research aims to achieve load balancing in IoT-SDN networks through a TOPSIS-based optimal server selection and resource allocation method. The proposed approach categorizes servers based on processing capacity and directs IoT requests accordingly. Experimental results demonstrate that this method improves load distribution, reduces response time, and enhances network performance. The findings indicate that integrating TOPSIS with SDN improves scalability, minimizes congestion, and optimizes resource utilization, making it an effective solution for managing large-scale IoT traffic.</p>

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Dynamic load balancing through TOPSIS based optimal server selection and resource allocation in SDN IoT network

  • 期刊论文

  • 发表日期：

    2025年8月4日

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  • A. Sandanasamy,
  • P. Joseph Charles

摘要

The Internet of Things (IoT) involves internet-connected devices capable of sharing information, leading to increased network traffic. Efficient network management requires flexibility, reliability, energy conservation, and effective data traffic handling for seamless service delivery. Load balancing helps solve many network issues by distributing traffic effectively and reducing congestion. As the traditional networks possess challenges in centralized control for device management and protocol updates, Software-Defined Networking manages network devices, offering comprehensive network control, separating data and control elements for programmability. This research aims to achieve load balancing in IoT-SDN networks through a TOPSIS-based optimal server selection and resource allocation method. The proposed approach categorizes servers based on processing capacity and directs IoT requests accordingly. Experimental results demonstrate that this method improves load distribution, reduces response time, and enhances network performance. The findings indicate that integrating TOPSIS with SDN improves scalability, minimizes congestion, and optimizes resource utilization, making it an effective solution for managing large-scale IoT traffic.